Machine learning for comprehensive forecasting of Alzheimer's Disease progression

Most approaches to machine learning from electronic health data can only predict a single endpoint.The ability to simultaneously simulate dozens of patient characteristics is a crucial step towardspersonalized medicine for Alzheimer's Disease. Here, we use an unsupervised machine learning modelcalled a Conditional Restricted Boltzmann Machine (CRBM) to simulate detailed patient trajectories.We use data comprising 18-month trajectories of 44 clinical variables from 1909 patients with MildCognitive Impairment or Alzheimer's Disease to train a model for personalized forecasting of diseaseprogression. We simulate synthetic patient data including the evolution of each sub-component ofcognitive exams, laboratory tests, and their associations with baseline clinical characteristics. Syntheticpatient data generated by the CRBM accurately refect the means, standard deviations, and correlationsof each variable over time to the extent that synthetic data cannot be distinguished from actual databy a logistic regression. Moreover, our unsupervised model predicts changes in total ADAS-Cog scoreswith the same accuracy as specifcally trained supervised models, additionally capturing the correlationstructure in the components of ADAS-Cog, and identifes sub-components associated with word recallas predictive of progression.

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Your Digital Twin - UnlearnAI

Podcasts

Using AI Digital Twins for Drug Testing

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Unlearn.AI nabs $12M to build “digital twins” to speed up and improve clinical trials

“Unlearn’s pioneering use of Digital Twins will limit the number of patients that need to go on placebo while also reducing overall trial enrollment time."
Dr. Charles Fisher, CEO of Unlearn AI, discusses creating digital clones by using artificial intelligence for use in clinical drug trials.
A fascinating approach to the problem of how to make clinical trials more efficient, and understand more about what may be possible with more and better patient data.

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